This thesis has developed an AI-driven decision support framework that enhances energy efficiency in energy-intensive industrial and transportation systems. The research is motivated by the high energy consumption and emissions of these sectors, as well as the untapped potential of their expanding data streams. It aims to establish a rigorous and interpretable framework that bridges the gap between data analytics and operational decision-making in energy-intensive sectors.